Modern consumers are inundated with choices. Electronic retailers and content providers offer huge selections of products, with unprecedented opportunities to meet a variety of special needs and tastes. Matching consumers with the most appropriate products is not trivial, yet is a key to enhancing user satisfaction and loyalty.
Recommender systems analyze patterns of user interest in items or products to provide personalized recommendations of items that will suit a user's taste. One particular challenge that recommender systems face is handling new users; i.e., the “user cold start problem.” The quality of recommendations strongly depends on the amount of data gathered from the user, making it difficult to generate reasonable recommendations to users new to the system. Yet, new users are crucial for the recommendation environment, and providing them with a good experience is essential to growing the user base of the system. Pleasing these new users is all the more challenging, as they often judge the system's value based on their first few experiences. This is particularly true for systems based on explicit user feedback, where users are required to actively provide ratings to the system in order to get useful suggestions.